Sustainability of Wind Energy under Changing Wind Regimes—A Case Study

A method was introduced to assess the sustainability of energy production over the lifetime (~20 y) of wind turbines. Community Earth System Model simulations were downscaled for the tourist seasons (mid-May to mid-September) of 2006 to 2012 (CESM-P1) and 2026 to 2032 (CESM-P2) to obtain a reference and projected wind-speed climatology, respectively. The wind speeds served to calculate the potential power output and capacity factors of seven turbine types. CESM-P1 wind-speed climatology, power output, and capacity factors were compared to those derived from wind speeds obtained by numerical weather forecasts for reference to known standard to wind-farm managers. Juneau, Alaska served as a virtual testbed as this region is known to experience changes in wind speeds in response to the Pacific Decadal Oscillation. CESM-P2 suggested about 2% decrease for wind speeds between the speeds at cut-in and rated power, and about 8% - 10% decrease in potential wind-power output. This means that in regions of decadal climate variations, the sustainability of wind-energy production should be part of the decision-making process. The study demonstrated that using mean values of wind-speeds can provide qualitative knowledge about decreases/increases in potential energy production, but not about the magnitude. Using the total individual wind-speed data of all seasons provided the same amount of total power output than summing up the power outputs of individual seasons. The main advantage of calculating individual seasonal wind-power outputs, however, is that it theoretically permits assessment of interannual variability in power output and capacity factors. Comparison to a known standard may help stakeholders in understanding of uncertainty and interpretation of projected changes.

Using Earth’s Moon as a Testbed for Quantifying the Effect of the Terrestrial Atmosphere

In the past, the planetary radiation balance served to quantify the atmospheric greenhouse effect by the difference between the globally averaged near-surface temperature of and the respective effective radiation temperature of the Earth without atmosphere of resulting in . Since such a “thought experiment” prohibits any rigorous assessment of its results, this study considered the Moon as a testbed for the Earth in the absence of its atmosphere. Since the angular velocity of Moon’s rotation is 27.4 times slower than that of the Earth, the forcing method, the force-restore method, and a multilayer-force-restore method, used in climate modeling during the past four decades, were alternatively applied to address the influence of the angular velocity in determining the Moon’s globally averaged skin (or slab) temperature, . The multilayer-force-restore method always provides?the highest values for , followed by the force-restore method and the forcing method, but the differences are marginal. Assuming a solar albedo of , a relative emissivity , and a solar constant of and applying the multilayer-force-restore method yielded and for the Moon. Using the same values for α, ε, and S, but assuming the Earth’s angular velocity for the Moon yielded and quantifying the effect of the terrestrial atmosphere by . A sensitivity study for a solar albedo of commonly assumed for the Earth in the absence of its atmosphere yielded , , and . This means that the atmospheric effect would be more than twice as large as the aforementioned difference of 33 K. To generalize the findings, twelve synodic months (i.e., 354 Earth days) and 365 Earth days, where , a Sun-zenith-distance dependent solar albedo, and the variation of the solar radiation in dependence of the actual orbit position and the tilt angle of the corresponding rotation axis to the ecliptic were considered. The case of Moon’s true angular velocity yielded and . Whereas Earth’s 27.4 times higher angular velocity yielded , and . In both cases, the effective radiation temperature is ,?because the computed global albedo is . Thus, the effective radiation temperature yields flawed results when used for quantifying the atmospheric greenhouse effect.

Meridional Distributions of Historical Zonal Averages and Their Use to Quantify the Global and Spheroidal Mean Near-Surface Temperature of the Terrestrial Atmosphere

The zonal averages of temperature (the so-called normal temperatures) for numerous parallels of latitude published between 1852 and 1913 by Dove, Forbes, Ferrel, Spitaler, Batchelder, Arrhenius, von Bezold, Hopfner, von Hann, and Börnstein were used to quantify the global (spherical) and spheroidal mean near-surface temperature of the terrestrial atmosphere. Only the datasets of Dove and Forbes published in the 1850s provided global averages below 〈T〉=14°C, mainly due to the poor coverage of the Southern Hemisphere by observations during that time. The global averages derived from the distributions of normal temperatures published between 1877 and 1913 ranged from 〈T〉=14.0°C (Batchelder) to 〈T〉=15.1°C (Ferrel). The differences between the global and the spheroidal mean near-surface air temperature are marginal. To examine the uncertainty due to interannual variability and different years considered in the historic zonal mean temperature distributions, the historical normal temperatures were perturbed within ±2σ to obtain ensembles of 50 realizations for each dataset. Numerical integrations of the perturbed distributions indicate uncertainties in the global averages in the range of ±0.3°C to ±0.6°C and depended on the number of available normal temperatures. Compared to our results, the global mean temperature of 〈T〉=15.0°C published by von Hann in 1897 and von Bezold in 1901 and 1906 is notably too high, while 〈T〉=14.4°C published by von Hann in 1908 seems to be more adequate within the range of uncertainty. The HadCRUT4 record provided 〈T〉≌?13.7°C for 1851-1880 and 〈T〉=13.6°C for 1881-1910. The Berkeley record provided 〈T〉=13.6°C and 〈T〉≌?13.5°C for these periods, respectively. The NASA GISS record yielded 〈T〉=13.6°C for 1881-1910 as well. These results are notably lower than those based on the historic zonal means. For 1991-2018, the HadCRUT4, Berkeley, and NASA GISS records provided 〈T〉=14.4°C, 〈T〉=14.5°C, and 〈T〉=14.5°C, respectively. The comparison of the 1991-2018 globally averaged near-surface temperature with those derived from distributions of zonal temperature averages for numerous parallels of latitude suggests no change for the past 100 years.

On the Solar Climate of the Moon and the Resulting Surface Temperature Distribution

The solar climate of our Moon is analyzed using the results of numerical simulations and the recently released data of the Diviner Lunar Radiometer Experiment (DLRE) to assess (a) the resulting distribution of the surface temperature, (b) the related global mean surface temperature Ts>, and (c) the effective radiation temperature Te often considered as a proxy for Ts> of rocky planets and/or their natural satellites, where Te is based on the global radiation budget of the well-known “thought model” of the Earth in the absence of its atmosphere. Because the Moon consists of similar rocky material like the Earth, it comes close to this thought model. However, the Moon’s astronomical features (e.g., obliquity, angular velocity of rotation, position relative to the disc of the solar system) differ from that of the Earth. Being tidally locked to the Earth, the Moon’s orbit around the Sun shows additional variation as compared to the Earth’s orbit. Since the astronomical parameters affect the solar climate, we predicted the Moon’s orbit coordinates both relative to the Sun and the Earth for a period of 20 lunations starting May 24, 2009, 00:00 UT1 with the planetary and lunar ephemeris DE430 of the Jet Propulsion Laboratory of the California Institute of Technology. The results revealed a mean heliocentric distance for the Moon and Earth of 1.00124279 AU and 1.00166376 AU, respectively. The mean geocentric distance of the Moon was 384792 km. The synodic and draconic months deviated from their respective means in a range of -5.7 h to 6.9 h and ±3.4 h, respectively. The deviations of the anomalistic months from their mean range between -2.83 d and 0.97 d with the largest negative deviations occurring around the points of inflection in the curve that represents the departure of the synodic month from its mean. Based on the two successive passages of the Sun through the ascending node of the lunar equator plane, the time interval between them corresponds to 347.29 days, i.e., it is slightly longer than the mean draconic year of 346.62 days. We computed the local solar insolation as input to the multilayer-force restore method of Kramm et al. (2017) that is based on the local energy budget equation. Due to the need to spin up the distribution of the regolith temperature to equilibrium, analysis of the model results covers only the last 12 lunations starting January 15, 2010, 07:11 UT1. The predicted slab temperatures, Tslab, considered as the realistic surface temperatures, follow the bolometric temperatures, Tbol, acceptably. According to all 24 DLRE datasets related to the subsolar longitude øss, the global averages of the bolometric temperature amounts to Tbol=201.1k± 0.6K. Based on the globally averaged emitted infrared radiation of FIR>=290.5W·m-2± 3.0W·m-2 derived from the 24 DLRE datasets, the effective radiative temperature of the Moon is Te, M>=Tbol>1/4=271.0k± 0.7K so that Tbol>≅0.742Te, M. The DLRE observations suggest that in the case of rocky planets and their natural satellites, the globally averaged surface temperature is notably lower than the effective radiation temperature. They differ by a factor that depends on the astronomical parameters especially on the angular velocity of rotation.

Near-Surface Wind-Speed Stilling in Alaska during 1984-2016 and Its Impact on the Sustainability of Wind Power

Based on wind-speed records of Alaska’s 19 first-order weather stations, we analyzed the near-surface wind-speed stilling for January 1, 1984 to December 31, 2016. With exception of Big Delta that indicates an increase of 0.0157 m·s–1·a–1, on average, all other first-order weather stations show declining trends in the near-surface wind speeds. In most cases, the average trends are less then?–0.0300?m·s–1·a–1. The strongest average trend of?–0.0500?m·s–1·a–1 occurred at Homer, followed by?–0.0492?m·s–1·a–1 at Bettles, and?–0.0453?m·s–1·a–1 at Yakutat, while the declining trend at Barrow is marginal. The impact of the near-surface wind-speed stilling on the wind-power potential expressed by the wind-power density was predicted and compared with the wind-power classification of the National Renewable Energy Laboratory and the Alaska Energy Authority. This wind-power potential is, however, of subordinate importance because wind turbines only extract a fraction of the kinetic energy from the wind field characterized by the power efficiency. Since wind turbine technology has notably improved during the past 35 years, we hypothetically used seven currently available wind turbines of different rated power and three different shear exponents to assess the wind-power sustainability under changing wind regimes. The shear exponents 1/10, 1/7, and 1/5 served to examine the range of wind power for various conditions of thermal stratification. Based on our analysis for January 1, 1984 to December 31, 2016, Cold Bay, St. Paul Island, Kotzebue, and Bethel would be very good candidates for wind farms. To quantify the impact of a changing wind regime on wind-power sustainability, we predicted wind power for the periods January 1, 1984 to December 31, 1994 and January 1, 2006 to December 31, 2016 as well. Besides Big Delta that suggests an increase in wind power of up to 12% for 1/7, predicted wind power decreased at all sites with the highest decline at Annette (≈38%), Kodiak (≈30%), King Salmon (≈26%), and Kotzebue (≈24%), where the effect of the shear exponents was marginal. Bethel (up to 20%) and Cold Bay (up to 14%) also show remarkable decreases in predicted wind power.

On the Maximum of Wind Power Efficiency

In our paper we demonstrate that the filtration equation used by Gorban’ et al. for determining the maximum efficiency of plane propellers of about 30 percent for free fluids plays no role in describing the flows in the atmospheric boundary layer (ABL) because the ABL is mainly governed by turbulent motions. We also demonstrate that the stream tube model customarily applied to derive the Rankine-Froude theorem must be corrected in the sense of Glauert to provide an appropriate value for the axial velocity at the rotor area. Including this correction leads to the Betz-Joukowsky limit, the maximum efficiency of 59.3 percent. Thus, Gorban’ et al.’s 30% value may be valid in water, but it has to be discarded for the atmosphere. We also show that Joukowsky’s constant circulation model leads to values of the maximum efficiency which are higher than the Betz-Jow-kowsky limit if the tip speed ratio is very low. Some of these values, however, have to be rejected for physical reasons. Based on Glauert’s optimum actuator disk, and the results of the blade-element analysis by Okulov and S&oslashrensen we also illustrate that the maximum efficiency of propeller-type wind turbines depends on tip-speed ratio and the number of blades.

Uncertainty of Wind Power Usage in Complex Terrain—A Case Study

This study investigated the uncertainty assessing wind-power production in valleys of complex terrain using Juneau, Alaska as the testbed. The wind-speed data stem from evaluated WRF/Chem simulations for seven tourist seasons (May 15 to September 15). The percentage of wind speeds between cut-in and cutout speed differed up to about 11% among tourist seasons and up to 15% among the examined wind-turbine types. The wind-speed probability density varied the strongest among tourist seasons for wind speeds less than 3 m&#183s&#451 (6 m&#183s&#451) for wind turbines with hub heights of about 80 m (30 m). At these heights, the interannual differences in the probability density of wind speeds at the rated or higher power were about half or less than those at wind speeds below 3 m&#183s&#451 (6 m&#183s&#451). The predicted average power output notably differed among tourist seasons. The tall (small) turbines had their highest predicted average production in 2006 (2012). The ranking among wind turbines regarding the predicted average power production was independent of the interannual variability in average power production. Capacity factors differed about 8% (6%) for the tall (small) tubines among tourist seasons. Within the same tourist season, capacity factors differed about 8% (5%) among turbine types. Estimates of capacity and potential power derived from 10 m wind-speed observations by an empirical formula commonly used to estimate wind speeds at hub height, differed up to 40% for 80 m height for some turbine types. Determinating the exponent of the empirical equation by means of WRF/Chem data showed that the traditional empirical approach failed in complex terrain.

Theoretical Investigations on Mapping Mean Distributions of Particulate Matter,Inert,Reactive,and Secondary Pollutants from Wildfires by Unmanned Air Vehicles(UAVs)

Evaluated Weather Research and Forecasting model inline with chemistry (WRF/Chem) simulations of the 2009 Crazy Mountain Complex wildfire in Interior Alaska served as a testbed for typical Alaska wildfire-smoke conditions. A virtual unmanned air vehicle (UAV) sampled temperatures, dewpoint temperatures, primary inert and reactive gases and particular matter of different sizes as well as secondary pollutants from the WRF/Chem results using different sampling patterns, altitudes and speeds to investigate the impact of the sampling design on obtained mean distributions. In this experimental design, the WRF/Chem data served as the “grand truth” to assess the mean distributions from sampling. During frontal passage, the obtained mean distributions were sensitive to the flight patterns, speeds and heights. For inert constituents mean distributions from sampling agreed with the “grand truth” within a factor of two at 1000 m. Mean distributions of gases involved in photochemistry differed among flight patterns except for ozone. The diurnal cycle of these gases’ concentrations led to overestimation (underestimation) of 20 h means in areas of high (low) concentrations as compared to the “grand truth.” The mean ozone distribution was sensitive to the speed of the virtual UAV. Particulate matter showed the strongest sensitivity to the flight patterns, especially during precipitation.